TY - GEN
T1 - An Optimized Solar PV Model Parameter Extraction Technique Using Lungs Performance-Based Optimization
AU - Khajuria, Rahul
AU - Sharma, Ananad Krishan
AU - Sharma, Pankaj
AU - Kumar, Rajesh
AU - Lamba, Ravita
AU - Raju, Saravanakumar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.
AB - Accurate determination of photovoltaic (PV) model parameters is essential for studying the variables that affect PV power generation efficiency. The extraction of parameters from the PV model presents a challenging problem due to its multi-model as well as nonlinear characteristics. This paper presents a solution to this challenge through the utilization of the Lungs Performance-Based Optimization (LPO) algorithm. The parameter identification technique has been described as an optimization problem focused on minimizing the current-based root mean squared error (RMSE). Additionally, the LPO algorithm is adopted in triple diode model (TDM) parameter extraction for RTC France, and Photowatt-PWP201 solar PV modules. The outcomes indicate that the LPO algorithm combined with the Newton–Raphson method demonstrates superior robustness and convergence accuracy contrasted to the other meta-heuristics (MH) algorithm with obtained minimum RMSE value of 7.3478E-04 and 2.0528E-03 for RTC France and Photowatt-PWP201 solar PV modules, respectively. Furthermore, an extensive performance evaluation of the MH algorithms has been performed, utilizing convergence curves, boxplots, as well as characteristic curves for current (I) versus voltage (V) and power (P) versus voltage (V), derived from the experimental outcomes.
KW - I–V characteristics
KW - Lungs performance-based optimization
KW - Parameter identification
KW - Solar PV
KW - Statistical study
UR - https://www.scopus.com/pages/publications/105016215476
U2 - 10.1007/978-981-96-5955-5_34
DO - 10.1007/978-981-96-5955-5_34
M3 - Conference contribution
AN - SCOPUS:105016215476
SN - 9789819659548
T3 - Lecture Notes in Networks and Systems
SP - 399
EP - 410
BT - Soft Computing
A2 - Kumar, Rajesh
A2 - Verma, Ajit Kumar
A2 - Verma, Om Prakash
A2 - Rajpurohit, Jitendra
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th International Conference on Soft Computing: Theories and Applications, SoCTA 2024
Y2 - 27 December 2024 through 29 December 2024
ER -
